1. Field of Invention
The use of application software, running on personal computers, to perform engineering computational tasks is becoming common. One task is to evaluate the health of a process control loop in industrial process situations.
A control loop has 3 main parts. These parts are: (1) the measurement of a process variable like flow, temperature or pressure, (2) a modulating control valve, variable speed pump or other manipulated device, (3) a controller that reads the measurement (part 1) and computes an output to the manipulated device (part 2).
Loop health is a combination of performance assessment criteria determined by the user. Loop health is the assessment of how a control loop is performing based on a definition of performance chosen by the end user. For example, a control loop consisting of a valve, flow measurement and controller may be considered to be performing well if the loop's flow measurement is consistently close to the set point over a period of time. A possible assessment could be the average value of the error (or difference) between the set point and flow measurement for a certain period of time. Another assessment may be the standard deviation of the measurement over a certain period of time. A third example of an assessment is the percentage time that the output of the control loop is at an extreme or limited value. The loop health could be specified by the user as any one of these assessments or some combination of them.
A processing plant like an oil refinery may have hundreds to thousands of control loops. Assessing the health of the loops can help the processing plant determine where to concentrate maintenance efforts. The problem is how to interpret multiple assessments and combine these into an overall assessment or health of the loop.
One approach to measuring loop health is to perform a weighted sum of several assessments. The disadvantage of this approach is that it is unclear how to compare the relative health of one loop to another. An assessment that is large for one loop may be normal, while the same value on another would indicate a problem.
There is a need for obtaining the relative health of control loops so they can be compared for the purpose of prioritizing them. It will be more important for the plant to schedule resources to concentrate on those loops with the poorest health.
2. Description of Prior Art
Over the years numerous methods have been developed to optimize processes in industrial plants. Many of these methods have focused on process control loops involving sensors which sense what is going on in the process, and controllers which change or regulate some parameter of the process such as flow rate, temperature, pressure, proportions for mixing chemicals, etc. Representative of prior art are the following patents. U.S. Pat. No. 4,649,515, Mar. 10, 1987 to Thompson et al. involves monitoring and diagnosing sensor and interactive based process systems. The knowledge base concerning the process system is in the form of a list in stored memory including evidence-hypothesis rules. The system detects malfunctions in an industrial system and modifies the operation of the system and provides users with information about probable causes of malfunctions in the system. U.S. Pat. No. 5,838,561, Nov. 17, 1998 to Owen involves a method of diagnosing a malfunction in a process control system which includes at least one closed loop control loop and comprises measuring a histogram of control loop tracking error, determining distortion of tracking error relative to a Gaussian distribution and indicating a malfunction when a deviation from the Gaussian distribution of tracking error exceeds predetermined limits. U.S. Pat. No. 6,298,454, Oct. 2, 2001 to Schleiss et al. involves a diagnostic tool which collects data involving a variability parameter, mode parameter, status parameter and limit parameter associated with each of the different devices, loops, or function blocks in a process control system and indicates to an operator a list of detected problems in the system. U.S. Pat. No. 6,459,939, Oct. 1, 2002 to Hugo involves a method for determining the performance of model predictive controllers requiring only closed loop data and an estimate of process deadtime. A good overview of the field is found in an article entitled “A practical approach for large-scale controller performance assessment, diagnosis and improvement” by Michael A. Paulonis and John W. Cox, Journal of Process Control, 13 (2003) 155–168. The above inventions have to do with either a calculation of specific assessments, or they provide an approach for combining several assessments that makes it difficult to compare loops to other loops. When looking at several loops, the health of a loop relative to other loops is important to process plants.